Engebretsen, Lars

ABSTRACT The International Consensus Conference “Optimising Performance of the Elite Athlete,” held in November 2024, brought together 29 scientists, some coaches, and athletes to establish evidence‐based consensus statements aimed at enhancing elite athletic performance and health. The conference addressed critical themes including training strategies, nutrition, female athlete considerations, injury management, and emerging technologies. Key conclusions emphasize individualized, sport‐specific approaches to training and nutrition, integrating concurrent training modalities to improve endurance, resilience, and efficiency. Nutrition strategies highlight the importance of tailored energy and macronutrient periodization, recognition of low energy availability risks, and cautious use of dietary supplements. Special attention was directed to female athletes, advocating for improved monitoring of menstrual cycles and hormonal status, while acknowledging current knowledge gaps in hormonal influences on performance and injury risk. Injury prevention remains a challenge, with tendon overuse and Achilles tendon ruptures significantly impacting athlete careers; rehabilitation should rely on criteria‐based progression and multidisciplinary input. Emerging technologies, including wearable sensors and multi‐omics analyses, hold promise for personalized training and nutrition but require further validation in elite contexts. Despite robust consensus, the panel identified substantial research gaps, particularly regarding female athletes, longitudinal training effects, and efficacy of novel interventions. This consensus provides a practical, scientifically grounded framework to optimize elite athlete performance and health, while underscoring the need for continued research to address outstanding questions and promote inclusive evidence‐based practices.

Multiligament knee injuries (MLKIs) represent a broad spectrum of pathology with potentially devastating consequences. Currently, disagreement in the terminology, diagnosis and treatment of these injuries limits clinical care and research. This study aimed to develop consensus on the nomenclature, diagnosis, treatment and rehabilitation strategies for patients with MLKI, while identifying important research priorities for further study. An international consensus process was conducted using validated Delphi methodology in line with British Journal of Sports Medicine guidelines. A multidisciplinary panel of 39 members from 14 countries, completed 3 rounds of online surveys exploring aspects of nomenclature, diagnosis, treatment, rehabilitation and future research priorities. Levels of agreement (LoA) with each statement were rated anonymously on a 5-point Likert scale, with experts encouraged to suggest modifications or additional statements. LoA for consensus in the final round were defined ‘a priori’ if >75% of respondents agreed and fewer than 10% disagreed, and dissenting viewpoints were recorded and discussed. After three Delphi rounds, 50 items (92.6%) reached consensus. Key statements that reached consensus within nomenclature included a clear definition for MLKI (LoA 97.4%) and the need for an updated MLKI classification system that classifies injury mechanism, extent of non-ligamentous structures injured and the presence or absence of dislocation. Within diagnosis, consensus was reached that there should be a low threshold for assessment with CT angiography for MLKI within a high-energy context and for certain injury patterns including bicruciate and PLC injuries (LoA 89.7%). The value of stress radiography or intraoperative fluoroscopy also reached consensus (LoA 89.7%). Within treatment, it was generally agreed that existing literature generally favours operative management of MLKI, particularly for young patients (LoA 100%), and that single-stage surgery should be performed whenever possible (LoA 92.3%). This consensus statement will facilitate clinical communication in MLKI, the care of these patients and future research within MLKI.

Objectives: The purpose of this study was to radiographically assess changes in 1) patellar height, 2) anterior tibial translation (ATT), and 3) posterior tibial slope following supratubercle ACW-PTO and to compare changes in patellar height to a cohort of medial opening proximal tibial osteotomies (MOW-PTO). Methods: Patients who underwent supratubercle ACW-PTO by a single surgeon from July 2019-February 2023 were included. Institutional Review Board approval was obtained for this study. Radiographic assessment at four time points (preoperative, postoperative day 1, 3 months and 6 months) of patellar height (via the Caton Dechamps and Insall Salvati ratios), posterior tibial slope, and anterior tibial translation of the lateral tibial plateau relative to the lateral femoral condyle was completed. A comparison cohort of patients undergoing MOW-PTO was included for comparison of patellar height. Results: Patellar height averages ( n=16) after ACW-PTO using the Caton-Deschamps ratio (Figure 1) demonstrated a significant increase in patellar height on postoperative day 1 ( p=0.004), but were not significantly different at 3 and 6 months postoperatively (Table 2). Conversely, averages of patellar height after MOW-PTO ( n=28) demonstrated no difference on postoperative day 1, but then was significantly decreased by 6 months postoperatively ( p=0.005) (Table 1). There was a significant difference between the closing and opening-wedge cohorts when comparing the difference in patellar height using the CD ratio between preoperative and 6 months ( p=0.011). Anterior tibial translation in the ACW-PTO cohort was significantly decreased by an average of 9.0 mm from preoperative to postoperative values ( p<<0.001) (Figure 2). Posterior tibial slope in the ACW-PTO cohort was significantly decreased by an average of 11.8° from preoperative to postoperative values ( p<<0.001). Conclusions: Supratubercle ACW-PTO performed for ACL failure in the setting of increased tibial slope did not induce significant changes in patellar height postoperatively. Furthermore, following ACW-PTO there was a significant decrease in anterior tibial translation and posterior tibial slope.

Purpose To develop a machine learning model capable of identifying subscapularis tears before surgery based on imaging and physical examination findings. Methods Between 2010 and 2020, 202 consecutive shoulders underwent arthroscopic rotator cuff repair by a single surgeon. Patient demographics, physical examination findings (including range of motion, weakness with internal rotation, lift/push‐off test, belly press test, and bear hug test), and imaging (including direct and indirect signs of tearing, biceps status, fatty atrophy, cystic changes, and other similar findings) were included for model creation. Results Sixty percent of the shoulders had partial or full thickness tears of the subscapularis verified during surgery (83% of these were upper third). Using only preoperative imaging‐related parameters, the XGBoost model demonstrated excellent performance at predicting subscapularis tears (c‐statistic, 0.84; accuracy, 0.85; F1 score, 0.87). The top 5 features included direct signs related to the presence of tearing as evidenced on magnetic resonance imaging (MRI) (changes in tendon morphology and signal), as well as the quality of the MRI and biceps pathology. Conclusions In this study, machine learning was successful in predicting subscapularis tears by MRI alone in 85% of patients, and this accuracy did not decrease by isolating the model to the top features. The top five features included direct signs related to the presence of tearing as evidenced on MRI (changes in tendon morphology and signal), as well as the quality of the MRI and biceps pathology. Last, in advanced modeling, the addition of physical examination or patient characteristics did not make a significant difference in the predictive ability of this model. Level of Evidence Level III, diagnostic case‐control study.

AbstractPurposeExternal validation of machine learning predictive models is achieved through evaluation of model performance on different groups of patients than were used for algorithm development. This important step is uncommonly performed, inhibiting clinical translation of newly developed models. Machine learning analysis of the Norwegian Knee Ligament Register (NKLR) recently led to the development of a tool capable of estimating the risk of anterior cruciate ligament (ACL) revision (https://swastvedt.shinyapps.io/calculator_rev/). The purpose of this study was to determine the external validity of the NKLR model by assessing algorithm performance when applied to patients from the Danish Knee Ligament Registry (DKLR).MethodsThe primary outcome measure of the NKLR model was probability of revision ACL reconstruction within 1, 2, and/or 5 years. For external validation, all DKLR patients with complete data for the five variables required for NKLR prediction were included. The five variables included graft choice, femur fixation device, KOOS QOL score at surgery, years from injury to surgery, and age at surgery. Predicted revision probabilities were calculated for all DKLR patients. The model performance was assessed using the same metrics as the NKLR study: concordance and calibration.ResultsIn total, 10,922 DKLR patients were included for analysis. Average follow‐up time or time‐to‐revision was 8.4 (± 4.3) years and overall revision rate was 6.9%. Surgical technique trends (i.e., graft choice and fixation devices) and injury characteristics (i.e., concomitant meniscus and cartilage pathology) were dissimilar between registries. The model produced similar concordance when applied to the DKLR population compared to the original NKLR test data (DKLR: 0.68; NKLR: 0.68–0.69). Calibration was poorer for the DKLR population at one and five years post primary surgery but similar to the NKLR at two years.ConclusionThe NKLR machine learning algorithm demonstrated similar performance when applied to patients from the DKLR, suggesting that it is valid for application outside of the initial patient population. This represents the first machine learning model for predicting revision ACL reconstruction that has been externally validated. Clinicians can use this in‐clinic calculator to estimate revision risk at a patient specific level when discussing outcome expectations pre‐operatively. While encouraging, it should be noted that the performance of the model on patients undergoing ACL reconstruction outside of Scandinavia remains unknown.Level of evidenceIII.

Background: Several factors are associated with an increased risk of anterior cruciate ligament (ACL) reconstruction revision. However, the ability to accurately translate these factors into a quantifiable risk of revision at a patient-specific level has remained elusive. We sought to determine if machine learning analysis of the Norwegian Knee Ligament Register (NKLR) can identify the most important risk factors associated with subsequent revision of primary ACL reconstruction and develop a clinically meaningful calculator for predicting revision of primary ACL reconstruction. Methods: Machine learning analysis was performed on the NKLR data set. The primary outcome was the probability of revision ACL reconstruction within 1, 2, and/or 5 years. Data were split randomly into training sets (75%) and test sets (25%). Four machine learning models were tested: Cox Lasso, survival random forest, generalized additive model, and gradient boosted regression. Concordance and calibration were calculated for all 4 models. Results: The data set included 24,935 patients, and 4.9% underwent a revision surgical procedure during a mean follow-up (and standard deviation) of 8.1 ± 4.1 years. All 4 models were well-calibrated, with moderate concordance (0.67 to 0.69). The Cox Lasso model required only 5 variables for outcome prediction. The other models either used more variables without an appreciable improvement in accuracy or had slightly lower accuracy overall. An in-clinic calculator was developed that can estimate the risk of ACL revision (Revision Risk Calculator). This calculator can quantify risk at a patient-specific level, with a plausible range from near 0% for low-risk patients to 20% for high-risk patients at 5 years. Conclusions: Machine learning analysis of a national knee ligament registry can predict the risk of ACL reconstruction revision with moderate accuracy. This algorithm supports the creation of an in-clinic calculator for point-of-care risk stratification based on the input of only 5 variables. Similar analysis using a larger or more comprehensive data set may improve the accuracy of risk prediction, and future studies incorporating patients who have experienced failure of ACL reconstruction but have not undergone subsequent revision may better predict the true risk of ACL reconstruction failure. Level of Evidence: Prognostic Level III . See Instructions for Authors for a complete description of levels of evidence.

Background: Increased lateral posterior tibial slope (LPTS) is associated with increased rates of anterior cruciate ligament (ACL) injury and failure of ACL reconstruction. It is unknown if ACL deficiency influences the developing proximal tibial physis and slope in skeletally immature patients through anterior tibial subluxation and abnormal force transmission. Purpose: To assess the natural history of LPTS in skeletally immature patients with an ACL-injured knee. Study Design: Case series; Level of evidence, 4. Methods: A total of 38 participants from a previous study on nonoperative management of ACL injury in skeletally immature patients were included. During the initial study, bilateral knee magnetic resonance imaging (MRI) was performed within 1 year of enrollment and again at final follow-up. All patients were younger than 13 years at the time of enrollment, and final follow-up occurred a mean 10 years after the injury. MRI scans were retrospectively reviewed by 2 reviewers to determine bilateral LPTS for each patient and each time point. Linear mixed models were used to assess LPTS differences between knees, change over time, and association with operational status. Subgroup analyses were performed for patients who remained nonoperated throughout the study. Results: A total of 22 patients had ACL reconstruction before final follow-up and 16 remained nonoperated. In the entire study population, the mean LPTS was higher in the injured knee than in the contralateral knee at final follow-up by 2.0° ( P < .001; 95% CI, 1.3°–2.6°). The mean LPTS increased significantly in the injured knee by 0.9° ( P = .042; 95% CI, 0.03°–1.7°), while the mean LPTS decreased in the contralateral knee by 0.4° ( P = .363; 95% CI, –0.8° to 0.4°). A significant difference in LPTS was also observed in the nonoperated subgroup. No significant association was observed between LPTS and operational status. Conclusion: Lateral posterior tibial slope increased more in the ACL-injured knee than in the contralateral uninjured knee in a group of skeletally immature patients. Lateral posterior tibial slope at baseline was not associated with the need for surgical reconstruction over the study period.

Objectives Cross-country (CC) skiing consists of two main techniques: classic and skating. Hip motion during the skating technique is similar to that in ice skating and is considered a risk factor for femoroacetabular impingement (FAI) in ice hockey players. We aimed to compare the presence of CAM-type FAI in a cohort of elite junior CC (EJCC) skiers with a control group of non-athlete (NA) high school students. Methods Observational cohort study: EJCC skiers and NAs were recruited from a sports school and a regular high school, respectively. Baseline demographics and training history were obtained via a questionnaire. Bilateral hip MRI was performed and the alpha angle was measured in three planes. CAM deformity was defined as an alpha angle>55° on at least two MRI planes per hip. Results A total of 20 EJCC skiers and 10 NAs participated. All participants were male, aged 16–19 years. Average training volume was 528.10±68.34 hours per year for the EJCC skiers compared with 153.50±57.09 for the NAs (p<0.001). The prevalence of CAM deformity in at least one hip on MRI was 50% in both groups (10 CC skiers and 5 NAs). The average alpha angles were 52.4±6.1° in the EJCC group and 52.5±4.9° in the NA group (p=0.94). Conclusion Radiographic evidence of CAM-type FAI was not more common in the CC skiers compared with NAs. The type, rather than volume or intensity of training, maybe a more important risk factor for the development of CAM-type FAI in young athletes.